from abc import ABCMeta, abstractmethod
import pandas as pd
import os, os.path
class DataHandler(object):
    """
    DataHandler is an abstract base class providing an interface for
    all subsequent (inherited) data handlers (both live and historic).

    The goal of a (derived) DataHandler object is to output a generated
    set of bars (OLHCVI) for each symbol requested. 

    This will replicate how a live strategy would function as current
    market data would be sent "down the pipe". Thus a historic and live
    system will be treated identically by the rest of the backtesting suite.
    """

    __metaclass__ = ABCMeta

    @abstractmethod
    def get_latest_bars(self, symbol, N=1):
        """
        Returns the last N bars from the latest_symbol list,
        or fewer if less bars are available.
        """
        raise NotImplementedError("Should implement get_latest_bars()")

    @abstractmethod
    def update_bars(self):
        """
        Pushes the latest bar to the latest symbol structure
        for all symbols in the symbol list.
        """
        raise NotImplementedError("Should implement update_bars()")
        
class HistoricCSVDataHandler(DataHandler):
    """
    HistoricCSVDataHandler is designed to read CSV files for
    each requested symbol from disk and provide an interface
    to obtain the "latest" bar in a manner identical to a live
    trading interface. 
    """

    def __init__(self, events, csv_dir, symbol_list):
        """
        Initialises the historic data handler by requesting
        the location of the CSV files and a list of symbols.

        It will be assumed that all files are of the form
        'symbol.csv', where symbol is a string in the list.

        Parameters:
        events - The Event Queue.
        csv_dir - Absolute directory path to the CSV files.
        symbol_list - A list of symbol strings.
        """
        self.events = events
        self.csv_dir = csv_dir
        self.symbol_list = symbol_list

        self.symbol_data = {}
        self.latest_symbol_data = {}
        self.continue_backtest = True       

        self._open_convert_csv_files()
        
    def _open_convert_csv_files(self):
            """
            Opens the CSV files from the data directory, converting
            them into pandas DataFrames within a symbol dictionary.

            For this handler it will be assumed that the data is
            taken from DTN IQFeed. Thus its format will be respected.
            """
            comb_index = None
            for s in self.symbol_list:
                # Load the CSV file with no header information, indexed on date
                self.symbol_data[s] = pd.io.parsers.read_csv(
                                          os.path.join(self.csv_dir, '%s.csv' % s),
                                          header=0, index_col=0, 
                                          names=['datetime','open','low','high','close','volume','oi']
                                      )

                # Combine the index to pad forward values
                if comb_index is None:
                    comb_index = self.symbol_data[s].index
                else:
                    comb_index.union(self.symbol_data[s].index)

                # Set the latest symbol_data to None
                self.latest_symbol_data[s] = []

            # Reindex the dataframes
            for s in self.symbol_list:
                self.symbol_data[s] = self.symbol_data[s].reindex(index=comb_index, method='pad').iterrows()


    def _get_new_bar(self, symbol):
            """
            Returns the latest bar from the data feed as a tuple of 
            (sybmbol, datetime, open, low, high, close, volume).
            """
            for b in self.symbol_data[symbol]:
                yield tuple([symbol, datetime.datetime.strptime(b[0], '%Y-%m-%d %H:%M:%S'), 
                            b[1][0], b[1][1], b[1][2], b[1][3], b[1][4]])
                            
    def get_latest_bars(self, symbol, N=1):
        """
        Returns the last N bars from the latest_symbol list,
        or N-k if less available.
        """
        try:
            bars_list = self.latest_symbol_data[symbol]
        except KeyError:
            print("That symbol is not available in the historical data set.")
        else:
            return bars_list[-N:]  
    
    def update_bars(self):
        """
        Pushes the latest bar to the latest_symbol_data structure
        for all symbols in the symbol list.
        """
        for s in self.symbol_list:
            try:
                bar = self._get_new_bar(s).next()
            except StopIteration:
                self.continue_backtest = False
            else:
                if bar is not None:
                    self.latest_symbol_data[s].append(bar)
        self.events.put(MarketEvent())
        